Overview

Brought to you by YData

Dataset statistics

Number of variables14
Number of observations3150
Missing cells0
Missing cells (%)0.0%
Duplicate rows165
Duplicate rows (%)5.2%
Total size in memory344.7 KiB
Average record size in memory112.0 B

Variable types

Numeric8
Categorical6

Alerts

Dataset has 165 (5.2%) duplicate rowsDuplicates
Age is highly overall correlated with Age_GroupHigh correlation
Age_Group is highly overall correlated with AgeHigh correlation
Call_Failure is highly overall correlated with Charge_Amount and 2 other fieldsHigh correlation
Charge_Amount is highly overall correlated with Call_FailureHigh correlation
Churn is highly overall correlated with ComplainsHigh correlation
Complains is highly overall correlated with ChurnHigh correlation
Customer_Value is highly overall correlated with Distinct_Called_Numbers and 3 other fieldsHigh correlation
Distinct_Called_Numbers is highly overall correlated with Call_Failure and 3 other fieldsHigh correlation
Frequency_of_SMS is highly overall correlated with Customer_ValueHigh correlation
Frequency_of_use is highly overall correlated with Call_Failure and 4 other fieldsHigh correlation
Seconds_of_Use is highly overall correlated with Customer_Value and 3 other fieldsHigh correlation
Status is highly overall correlated with Frequency_of_use and 1 other fieldsHigh correlation
Complains is highly imbalanced (61.0%) Imbalance
Tariff_Plan is highly imbalanced (60.6%) Imbalance
Call_Failure has 702 (22.3%) zeros Zeros
Charge_Amount has 1768 (56.1%) zeros Zeros
Seconds_of_Use has 154 (4.9%) zeros Zeros
Frequency_of_use has 154 (4.9%) zeros Zeros
Frequency_of_SMS has 603 (19.1%) zeros Zeros
Distinct_Called_Numbers has 154 (4.9%) zeros Zeros
Customer_Value has 132 (4.2%) zeros Zeros

Reproduction

Analysis started2025-03-16 13:45:46.112114
Analysis finished2025-03-16 13:45:51.496279
Duration5.38 seconds
Software versionydata-profiling vv4.14.0
Download configurationconfig.json

Variables

Call_Failure
Real number (ℝ)

High correlation  Zeros 

Distinct37
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.6279365
Minimum0
Maximum36
Zeros702
Zeros (%)22.3%
Negative0
Negative (%)0.0%
Memory size24.7 KiB
2025-03-16T19:15:51.569343image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median6
Q312
95-th percentile22
Maximum36
Range36
Interquartile range (IQR)11

Descriptive statistics

Standard deviation7.2638856
Coefficient of variation (CV)0.95227399
Kurtosis0.90682067
Mean7.6279365
Median Absolute Deviation (MAD)5
Skewness1.0897518
Sum24028
Variance52.764034
MonotonicityNot monotonic
2025-03-16T19:15:51.658946image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0 702
22.3%
5 244
 
7.7%
7 166
 
5.3%
6 161
 
5.1%
8 156
 
5.0%
9 149
 
4.7%
3 141
 
4.5%
2 137
 
4.3%
4 133
 
4.2%
11 125
 
4.0%
Other values (27) 1036
32.9%
ValueCountFrequency (%)
0 702
22.3%
1 121
 
3.8%
2 137
 
4.3%
3 141
 
4.5%
4 133
 
4.2%
5 244
 
7.7%
6 161
 
5.1%
7 166
 
5.3%
8 156
 
5.0%
9 149
 
4.7%
ValueCountFrequency (%)
36 2
 
0.1%
35 2
 
0.1%
34 3
 
0.1%
33 3
 
0.1%
32 8
0.3%
31 6
 
0.2%
30 16
0.5%
29 7
0.2%
28 17
0.5%
27 13
0.4%

Complains
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size153.9 KiB
0
2909 
1
 
241

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3150
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2909
92.3%
1 241
 
7.7%

Length

2025-03-16T19:15:51.732616image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-16T19:15:51.781195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 2909
92.3%
1 241
 
7.7%

Most occurring characters

ValueCountFrequency (%)
0 2909
92.3%
1 241
 
7.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3150
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2909
92.3%
1 241
 
7.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3150
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2909
92.3%
1 241
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3150
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2909
92.3%
1 241
 
7.7%

Subscription_Length
Real number (ℝ)

Distinct45
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.541905
Minimum3
Maximum47
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.7 KiB
2025-03-16T19:15:51.888496image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile13
Q130
median35
Q338
95-th percentile42
Maximum47
Range44
Interquartile range (IQR)8

Descriptive statistics

Standard deviation8.5734821
Coefficient of variation (CV)0.26345975
Kurtosis1.2158424
Mean32.541905
Median Absolute Deviation (MAD)4
Skewness-1.300015
Sum102507
Variance73.504595
MonotonicityNot monotonic
2025-03-16T19:15:51.969828image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
36 276
 
8.8%
38 258
 
8.2%
37 229
 
7.3%
35 228
 
7.2%
39 201
 
6.4%
34 201
 
6.4%
40 186
 
5.9%
33 152
 
4.8%
32 121
 
3.8%
41 110
 
3.5%
Other values (35) 1188
37.7%
ValueCountFrequency (%)
3 8
 
0.3%
4 4
 
0.1%
5 6
 
0.2%
6 8
 
0.3%
7 19
0.6%
8 12
0.4%
9 22
0.7%
10 16
0.5%
11 26
0.8%
12 19
0.6%
ValueCountFrequency (%)
47 1
 
< 0.1%
46 13
 
0.4%
45 23
 
0.7%
44 44
 
1.4%
43 56
 
1.8%
42 80
 
2.5%
41 110
3.5%
40 186
5.9%
39 201
6.4%
38 258
8.2%

Charge_Amount
Real number (ℝ)

High correlation  Zeros 

Distinct11
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.94285714
Minimum0
Maximum10
Zeros1768
Zeros (%)56.1%
Negative0
Negative (%)0.0%
Memory size24.7 KiB
2025-03-16T19:15:52.029274image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile4
Maximum10
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.5210719
Coefficient of variation (CV)1.6132581
Kurtosis8.8543583
Mean0.94285714
Median Absolute Deviation (MAD)0
Skewness2.5848682
Sum2970
Variance2.3136597
MonotonicityNot monotonic
2025-03-16T19:15:52.072850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 1768
56.1%
1 617
 
19.6%
2 395
 
12.5%
3 199
 
6.3%
4 76
 
2.4%
5 30
 
1.0%
8 19
 
0.6%
9 14
 
0.4%
7 14
 
0.4%
6 11
 
0.3%
ValueCountFrequency (%)
0 1768
56.1%
1 617
 
19.6%
2 395
 
12.5%
3 199
 
6.3%
4 76
 
2.4%
5 30
 
1.0%
6 11
 
0.3%
7 14
 
0.4%
8 19
 
0.6%
9 14
 
0.4%
ValueCountFrequency (%)
10 7
 
0.2%
9 14
 
0.4%
8 19
 
0.6%
7 14
 
0.4%
6 11
 
0.3%
5 30
 
1.0%
4 76
 
2.4%
3 199
 
6.3%
2 395
12.5%
1 617
19.6%

Seconds_of_Use
Real number (ℝ)

High correlation  Zeros 

Distinct1756
Distinct (%)55.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4472.4597
Minimum0
Maximum17090
Zeros154
Zeros (%)4.9%
Negative0
Negative (%)0.0%
Memory size24.7 KiB
2025-03-16T19:15:52.138303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile54.5
Q11391.25
median2990
Q36478.25
95-th percentile15020.5
Maximum17090
Range17090
Interquartile range (IQR)5087

Descriptive statistics

Standard deviation4197.9087
Coefficient of variation (CV)0.93861297
Kurtosis0.99367573
Mean4472.4597
Median Absolute Deviation (MAD)1996
Skewness1.3219429
Sum14088248
Variance17622437
MonotonicityNot monotonic
2025-03-16T19:15:52.218345image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 154
 
4.9%
305 37
 
1.2%
710 9
 
0.3%
2088 9
 
0.3%
1015 9
 
0.3%
1973 9
 
0.3%
955 8
 
0.3%
2393 8
 
0.3%
1360 8
 
0.3%
650 8
 
0.3%
Other values (1746) 2891
91.8%
ValueCountFrequency (%)
0 154
4.9%
8 1
 
< 0.1%
13 1
 
< 0.1%
33 1
 
< 0.1%
50 1
 
< 0.1%
60 1
 
< 0.1%
73 1
 
< 0.1%
80 1
 
< 0.1%
88 1
 
< 0.1%
93 2
 
0.1%
ValueCountFrequency (%)
17090 1
< 0.1%
16980 1
< 0.1%
16785 1
< 0.1%
16675 1
< 0.1%
16640 1
< 0.1%
16570 1
< 0.1%
16560 1
< 0.1%
16500 1
< 0.1%
16495 1
< 0.1%
16480 1
< 0.1%

Frequency_of_use
Real number (ℝ)

High correlation  Zeros 

Distinct242
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.460635
Minimum0
Maximum255
Zeros154
Zeros (%)4.9%
Negative0
Negative (%)0.0%
Memory size24.7 KiB
2025-03-16T19:15:52.298945image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q127
median54
Q395
95-th percentile184.55
Maximum255
Range255
Interquartile range (IQR)68

Descriptive statistics

Standard deviation57.413308
Coefficient of variation (CV)0.82655893
Kurtosis0.82012484
Mean69.460635
Median Absolute Deviation (MAD)33
Skewness1.1441664
Sum218801
Variance3296.2879
MonotonicityNot monotonic
2025-03-16T19:15:52.376779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 154
 
4.9%
6 49
 
1.6%
44 38
 
1.2%
39 37
 
1.2%
41 35
 
1.1%
36 33
 
1.0%
33 33
 
1.0%
19 32
 
1.0%
45 32
 
1.0%
47 32
 
1.0%
Other values (232) 2675
84.9%
ValueCountFrequency (%)
0 154
4.9%
1 9
 
0.3%
2 15
 
0.5%
3 4
 
0.1%
4 23
 
0.7%
5 15
 
0.5%
6 49
 
1.6%
7 19
 
0.6%
8 25
 
0.8%
9 16
 
0.5%
ValueCountFrequency (%)
255 1
 
< 0.1%
254 2
 
0.1%
252 1
 
< 0.1%
250 2
 
0.1%
249 1
 
< 0.1%
248 2
 
0.1%
247 1
 
< 0.1%
246 2
 
0.1%
245 1
 
< 0.1%
244 5
0.2%

Frequency_of_SMS
Real number (ℝ)

High correlation  Zeros 

Distinct405
Distinct (%)12.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.174921
Minimum0
Maximum522
Zeros603
Zeros (%)19.1%
Negative0
Negative (%)0.0%
Memory size24.7 KiB
2025-03-16T19:15:52.547075image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median21
Q387
95-th percentile356.55
Maximum522
Range522
Interquartile range (IQR)81

Descriptive statistics

Standard deviation112.23756
Coefficient of variation (CV)1.5338255
Kurtosis3.2585401
Mean73.174921
Median Absolute Deviation (MAD)21
Skewness1.9741418
Sum230501
Variance12597.27
MonotonicityNot monotonic
2025-03-16T19:15:52.722163image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 603
 
19.1%
7 194
 
6.2%
9 54
 
1.7%
8 54
 
1.7%
15 54
 
1.7%
16 51
 
1.6%
17 47
 
1.5%
10 44
 
1.4%
12 42
 
1.3%
1 41
 
1.3%
Other values (395) 1966
62.4%
ValueCountFrequency (%)
0 603
19.1%
1 41
 
1.3%
2 39
 
1.2%
3 32
 
1.0%
4 30
 
1.0%
5 29
 
0.9%
6 26
 
0.8%
7 194
 
6.2%
8 54
 
1.7%
9 54
 
1.7%
ValueCountFrequency (%)
522 1
 
< 0.1%
515 1
 
< 0.1%
511 1
 
< 0.1%
508 1
 
< 0.1%
505 1
 
< 0.1%
504 1
 
< 0.1%
501 1
 
< 0.1%
500 1
 
< 0.1%
499 1
 
< 0.1%
498 3
0.1%

Distinct_Called_Numbers
Real number (ℝ)

High correlation  Zeros 

Distinct92
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.509841
Minimum0
Maximum97
Zeros154
Zeros (%)4.9%
Negative0
Negative (%)0.0%
Memory size24.7 KiB
2025-03-16T19:15:52.861423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q110
median21
Q334
95-th percentile51
Maximum97
Range97
Interquartile range (IQR)24

Descriptive statistics

Standard deviation17.217337
Coefficient of variation (CV)0.73234597
Kurtosis1.3599904
Mean23.509841
Median Absolute Deviation (MAD)11
Skewness1.0294021
Sum74056
Variance296.43671
MonotonicityNot monotonic
2025-03-16T19:15:52.927645image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 154
 
4.9%
2 88
 
2.8%
10 78
 
2.5%
15 77
 
2.4%
17 76
 
2.4%
6 76
 
2.4%
8 75
 
2.4%
20 75
 
2.4%
19 75
 
2.4%
16 74
 
2.3%
Other values (82) 2302
73.1%
ValueCountFrequency (%)
0 154
4.9%
1 31
 
1.0%
2 88
2.8%
3 44
 
1.4%
4 63
2.0%
5 60
 
1.9%
6 76
2.4%
7 61
 
1.9%
8 75
2.4%
9 73
2.3%
ValueCountFrequency (%)
97 1
 
< 0.1%
95 1
 
< 0.1%
93 1
 
< 0.1%
88 1
 
< 0.1%
87 1
 
< 0.1%
86 3
 
0.1%
85 3
 
0.1%
84 4
0.1%
83 4
0.1%
82 8
0.3%

Age_Group
Categorical

High correlation 

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size153.9 KiB
3
1425 
2
1037 
4
395 
5
170 
1
 
123

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3150
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row2
3rd row3
4th row1
5th row1

Common Values

ValueCountFrequency (%)
3 1425
45.2%
2 1037
32.9%
4 395
 
12.5%
5 170
 
5.4%
1 123
 
3.9%

Length

2025-03-16T19:15:52.994122image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-16T19:15:53.034838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3 1425
45.2%
2 1037
32.9%
4 395
 
12.5%
5 170
 
5.4%
1 123
 
3.9%

Most occurring characters

ValueCountFrequency (%)
3 1425
45.2%
2 1037
32.9%
4 395
 
12.5%
5 170
 
5.4%
1 123
 
3.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3150
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 1425
45.2%
2 1037
32.9%
4 395
 
12.5%
5 170
 
5.4%
1 123
 
3.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3150
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 1425
45.2%
2 1037
32.9%
4 395
 
12.5%
5 170
 
5.4%
1 123
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3150
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 1425
45.2%
2 1037
32.9%
4 395
 
12.5%
5 170
 
5.4%
1 123
 
3.9%

Tariff_Plan
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size153.9 KiB
1
2905 
2
 
245

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3150
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 2905
92.2%
2 245
 
7.8%

Length

2025-03-16T19:15:53.092438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-16T19:15:53.133383image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 2905
92.2%
2 245
 
7.8%

Most occurring characters

ValueCountFrequency (%)
1 2905
92.2%
2 245
 
7.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3150
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 2905
92.2%
2 245
 
7.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3150
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 2905
92.2%
2 245
 
7.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3150
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 2905
92.2%
2 245
 
7.8%

Status
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size153.9 KiB
1
2368 
2
782 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3150
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 2368
75.2%
2 782
 
24.8%

Length

2025-03-16T19:15:53.243254image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-16T19:15:53.324821image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 2368
75.2%
2 782
 
24.8%

Most occurring characters

ValueCountFrequency (%)
1 2368
75.2%
2 782
 
24.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3150
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 2368
75.2%
2 782
 
24.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3150
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 2368
75.2%
2 782
 
24.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3150
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 2368
75.2%
2 782
 
24.8%

Age
Categorical

High correlation 

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size157.0 KiB
30
1425 
25
1037 
45
395 
55
170 
15
 
123

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters6300
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row30
2nd row25
3rd row30
4th row15
5th row15

Common Values

ValueCountFrequency (%)
30 1425
45.2%
25 1037
32.9%
45 395
 
12.5%
55 170
 
5.4%
15 123
 
3.9%

Length

2025-03-16T19:15:53.365930image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-16T19:15:53.414813image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
30 1425
45.2%
25 1037
32.9%
45 395
 
12.5%
55 170
 
5.4%
15 123
 
3.9%

Most occurring characters

ValueCountFrequency (%)
5 1895
30.1%
3 1425
22.6%
0 1425
22.6%
2 1037
16.5%
4 395
 
6.3%
1 123
 
2.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6300
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 1895
30.1%
3 1425
22.6%
0 1425
22.6%
2 1037
16.5%
4 395
 
6.3%
1 123
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6300
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 1895
30.1%
3 1425
22.6%
0 1425
22.6%
2 1037
16.5%
4 395
 
6.3%
1 123
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6300
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 1895
30.1%
3 1425
22.6%
0 1425
22.6%
2 1037
16.5%
4 395
 
6.3%
1 123
 
2.0%

Customer_Value
Real number (ℝ)

High correlation  Zeros 

Distinct2654
Distinct (%)84.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean470.97292
Minimum0
Maximum2165.28
Zeros132
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size24.7 KiB
2025-03-16T19:15:53.474687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10.335
Q1113.80125
median228.48
Q3788.38875
95-th percentile1587.68
Maximum2165.28
Range2165.28
Interquartile range (IQR)674.5875

Descriptive statistics

Standard deviation517.01543
Coefficient of variation (CV)1.0977604
Kurtosis1.2244965
Mean470.97292
Median Absolute Deviation (MAD)160.5825
Skewness1.4272916
Sum1483564.7
Variance267304.96
MonotonicityNot monotonic
2025-03-16T19:15:53.780739image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 132
 
4.2%
45.495 11
 
0.3%
40.44 10
 
0.3%
15.165 6
 
0.2%
25.275 5
 
0.2%
180 4
 
0.1%
131.4 4
 
0.1%
121.4 4
 
0.1%
1538.145 4
 
0.1%
191.92 3
 
0.1%
Other values (2644) 2967
94.2%
ValueCountFrequency (%)
0 132
4.2%
2.34 1
 
< 0.1%
4 1
 
< 0.1%
4.41 1
 
< 0.1%
4.5 2
 
0.1%
5.13 1
 
< 0.1%
5.175 1
 
< 0.1%
5.4 3
 
0.1%
5.625 1
 
< 0.1%
5.94 1
 
< 0.1%
ValueCountFrequency (%)
2165.28 1
< 0.1%
2149.28 1
< 0.1%
2148.84 1
< 0.1%
2148.03 1
< 0.1%
2140.96 1
< 0.1%
2129.535 1
< 0.1%
2127.68 1
< 0.1%
2124.84 1
< 0.1%
2120.67 1
< 0.1%
2117.72 1
< 0.1%

Churn
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size153.9 KiB
0
2655 
1
495 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3150
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2655
84.3%
1 495
 
15.7%

Length

2025-03-16T19:15:53.854084image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-16T19:15:53.886036image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 2655
84.3%
1 495
 
15.7%

Most occurring characters

ValueCountFrequency (%)
0 2655
84.3%
1 495
 
15.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3150
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2655
84.3%
1 495
 
15.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3150
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2655
84.3%
1 495
 
15.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3150
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2655
84.3%
1 495
 
15.7%

Interactions

2025-03-16T19:15:50.754178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T19:15:46.549695image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T19:15:47.068122image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T19:15:47.718803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T19:15:48.355234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T19:15:48.945990image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T19:15:49.524306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T19:15:50.232983image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T19:15:50.812550image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T19:15:46.615294image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T19:15:47.134204image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T19:15:47.875145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T19:15:48.436567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T19:15:49.019931image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T19:15:49.587705image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T19:15:50.290865image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T19:15:50.869618image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T19:15:46.680815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T19:15:47.199391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T19:15:47.951298image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T19:15:48.501921image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T19:15:49.085743image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T19:15:49.648279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T19:15:50.357795image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T19:15:50.927333image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T19:15:46.755089image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T19:15:47.368619image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T19:15:48.007936image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T19:15:48.576777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T19:15:49.157165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T19:15:49.714231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T19:15:50.430872image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T19:15:50.985275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T19:15:46.822588image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T19:15:47.438603image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T19:15:48.065919image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T19:15:48.650280image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T19:15:49.232435image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T19:15:49.779212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T19:15:50.504774image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T19:15:51.043203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T19:15:46.886347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T19:15:47.496178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T19:15:48.134185image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T19:15:48.724715image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T19:15:49.298632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T19:15:49.844510image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T19:15:50.563092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T19:15:51.101389image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T19:15:46.944469image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T19:15:47.554995image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T19:15:48.207587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T19:15:48.798686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T19:15:49.372315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T19:15:50.108733image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T19:15:50.638068image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T19:15:51.167861image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T19:15:47.010471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T19:15:47.645254image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T19:15:48.280576image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T19:15:48.864156image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T19:15:49.461886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T19:15:50.166545image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-16T19:15:50.698991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-03-16T19:15:53.936350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AgeAge_GroupCall_FailureCharge_AmountChurnComplainsCustomer_ValueDistinct_Called_NumbersFrequency_of_SMSFrequency_of_useSeconds_of_UseStatusSubscription_LengthTariff_Plan
Age1.0001.0000.1030.2360.1320.0690.2140.2410.1730.2350.2970.1970.1620.192
Age_Group1.0001.0000.1030.2360.1320.0690.2140.2410.1730.2350.2970.1970.1620.192
Call_Failure0.1030.1031.0000.5720.0350.1700.3460.5140.2700.5500.4660.1210.2490.224
Charge_Amount0.2360.2360.5721.0000.1760.0630.3930.4370.3190.4470.4900.3210.1500.367
Churn0.1320.1320.0350.1761.0000.5300.3180.2960.2530.3380.3530.4980.2170.103
Complains0.0690.0690.1700.0630.5301.0000.1430.0780.1250.1430.1580.2690.1370.000
Customer_Value0.2140.2140.3460.3930.3180.1431.0000.5630.7800.6730.7140.4980.1450.434
Distinct_Called_Numbers0.2410.2410.5140.4370.2960.0780.5631.0000.3210.8240.7630.4470.1570.219
Frequency_of_SMS0.1730.1730.2700.3190.2530.1250.7800.3211.0000.3060.3080.3440.1040.452
Frequency_of_use0.2350.2350.5500.4470.3380.1430.6730.8240.3061.0000.9370.5360.1740.422
Seconds_of_Use0.2970.2970.4660.4900.3530.1580.7140.7630.3080.9371.0000.5960.1450.312
Status0.1970.1970.1210.3210.4980.2690.4980.4470.3440.5360.5961.0000.1930.162
Subscription_Length0.1620.1620.2490.1500.2170.1370.1450.1570.1040.1740.1450.1931.0000.230
Tariff_Plan0.1920.1920.2240.3670.1030.0000.4340.2190.4520.4220.3120.1620.2301.000

Missing values

2025-03-16T19:15:51.266770image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-16T19:15:51.405782image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Call_FailureComplainsSubscription_LengthCharge_AmountSeconds_of_UseFrequency_of_useFrequency_of_SMSDistinct_Called_NumbersAge_GroupTariff_PlanStatusAgeCustomer_ValueChurn
08038043707151731130197.6400
1003903185742122546.0350
210037024536035924311301536.5200
310038041986613511115240.0200
43038023935823311115145.8050
5110381377582322831130282.2800
64038023603928518311301235.9600
713037291151211444331130945.4400
8703801377316904431130557.6800
97038145158322531130191.9200
Call_FailureComplainsSubscription_LengthCharge_AmountSeconds_of_UseFrequency_of_useFrequency_of_SMSDistinct_Called_NumbersAge_GroupTariff_PlanStatusAgeCustomer_ValueChurn
314016029010053117931230109.440
3141502801130162854124598.650
3142150271153038261521125187.560
314370271353067152531130203.880
314470201200032351631130221.280
31452101926697147924422125721.980
31461701719237177804251155261.210
3147130184315751382131130280.320
31487011246954622212311301077.640
3149811121792257931130100.681

Duplicate rows

Most frequently occurring

Call_FailureComplainsSubscription_LengthCharge_AmountSeconds_of_UseFrequency_of_useFrequency_of_SMSDistinct_Called_NumbersAge_GroupTariff_PlanStatusAgeCustomer_ValueChurn# duplicates
29003500000212250.00016
42003700000212250.00006
24003400000511550.00005
43003700000212250.00015
22003400000212250.00014
28003500000212250.00004
33003600000212250.00004
85503903056722122545.49514
10070408534208831130996.76003
200160139020211221125157.95003